With the Development of Research, New Algorithms and Technologies Have Emerged, One of Which is Machine Learning. Machine Learning Can Extract Key Factors From Vast Amounts of Data, Identify Underlying Patterns, and Predict Future Trends. In Recent Years, Machine Learning Has Been Widely Used in

NCT07121309 | Completed

• 1 other identifier: JD-HG-2025-022
• Study Type: observational
• Target: 901
• Locations: 1 country
• Sites: 1

Brief Summary

The aim of this study is to construct a predictive model for postoperative delirium in elderly patients with hip fractures. The main question it answers is to construct a risk prediction model for hip fractures in the elderly through six machine learning methods, compare which method's model is better, and conduct external validation of the model's stability to provide a reference for the early clinical detection of postoperative delirium in elderly hip fracture patients. The clinical data of elderly patients with hip fractures have been collected in clinical practice and the model has been constructed.

Conditions

Delirium

Keywords

Hip fracture; Prediction model

Outcome Measures

Primary Outcomes (1)

• Postoperative delirium

  The Memory and Delirium Assessment Scale (MDAS) was used for delirium risk screening and assessment, including consciousness, attention, speech, behavior, and sleep aspects.

  The day after the operation

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients over 60 years old who undergo surgical treatment for hip fractures

You may qualify if:

• Age ≥ 60 years; diagnosed with hip fracture by X-ray; patients who underwent surgical treatment.

You may not qualify if:

• Patients with other severe diseases (Patients who reach grade IV or higher according to the American Society of Anesthesiologists (ASA) health status classification;Suffer from end-stage diseases;there is multiple organ dysfunction syndrome (MODS) or single organ failure); patients with mental disorders; patients participating in other studies.

Sponsors & Collaborators

• Second Affiliated Hospital of Soochow University: lead

Study Sites (1)

SecondSoochowU

Suzhou, Jiangsu, 215004, China

Location

MeSH Terms

Conditions

Delirium; Hip Fractures

Condition Hierarchy (Ancestors)

Confusion; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Neurocognitive Disorders; Mental Disorders; Femoral Fractures; Fractures, Bone; Wounds and Injuries; Hip Injuries; Leg Injuries

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 6, 2025
• First Posted: August 13, 2025
• Study Start: October 17, 2024
• Primary Completion: March 3, 2025
• Study Completion: March 3, 2025
• Last Updated: August 13, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)